Automated Dead Chicken Detection in Poultry Farms Using Knowledge Distillation and Vision Transformers
Detecting dead chickens in broiler farms is critical for maintaining animal welfare and preventing disease outbreaks. This study presents an automated system that leverages CCTV footage to detect dead chickens, utilizing a two-step approach to improve detection accuracy and efficiency. First, statio...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/1/136 |
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| Summary: | Detecting dead chickens in broiler farms is critical for maintaining animal welfare and preventing disease outbreaks. This study presents an automated system that leverages CCTV footage to detect dead chickens, utilizing a two-step approach to improve detection accuracy and efficiency. First, stationary regions in the footage—likely representing dead chickens—are identified. Then, a deep learning classifier, enhanced through knowledge distillation, confirms whether the detected stationary object is indeed a chicken. EfficientNet-B0 is employed as the teacher model, while DeiT-Tiny functions as the student model, balancing high accuracy and computational efficiency. A dynamic frame selection strategy optimizes resource usage by adjusting monitoring intervals based on the chickens’ age, ensuring real-time performance in resource-constrained environments. This method addresses key challenges such as the lack of explicit annotations for dead chickens, along with common farm issues like lighting variations, occlusions, cluttered backgrounds, chicken growth, and camera distortions. The experimental results demonstrate validation accuracies of 99.3% for the teacher model and 98.7% for the student model, with significant reductions in computational demands. The system’s robustness and scalability make it suitable for large-scale farm deployment, minimizing the need for labor-intensive manual inspections. Future work will explore integrating deep learning methods that incorporate temporal attention mechanisms and automated removal processes. |
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| ISSN: | 2076-3417 |